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Key Responsibilities
• Strong Postgre experience in AWS with prior DB2 to Postgre migration preferable
• CDC strategy & build: Change Data Capture pipelines (IBM CDC tools where needed); design subscriptions, bookmarks, resync, backfill/replay strategies.
• Data modeling & transformation: Translate Db2 schemas to Aurora Postgres; logical and physical models, data types, RI, constraints; when to denormalize.
• Integration pipelines: Db2 → CDC → Kafka/S3 → Aurora with UPSERT/MERGE patterns; idempotency, ordering, exactly/at-least-once semantics.
• Data encoding & types: EBCDIC→UTF-8, packed decimal/binary numerics; deterministic transformations with validation test suites.
• Migration tools: Schema conversion tooling; Glue/Athena/Redshift for downstream analytics; IaC (Terraform), CI/CD (GitLab).
• Cutover & controls: Dual-run validation, reconciliation (counts, checksums, sampling), rollback plans; lineage, masking, encryption, IAM.
• Observability: Lag, throughput, error rate, and cost dashboards (CloudWatch/Grafana); operational runbooks and actionable alerts.
Essential skills/knowledge/experience:
• Change Data Capture: CDC design and operations (IBM, Precisely, or equivalent); subscription management, bookmarks, replay, backfill.
• Db2 & z/OS knowledge: Db2 catalog, z/OS fundamentals, batch windows, performance considerations.
• Relational modeling: PostgreSQL/Aurora data modeling; normalization, indexing, partitioning; OLTP vs. analytics trade-offs.
• Integration patterns: Kafka/ hands-on, CDC-to-target pipelines, UPSERT/MERGE logic; Python/SQL; strong troubleshooting.
• Data quality mindset: Write validation tests before migration; golden-source reconciliation.
• Data Architecture Fundamentals (Must-Have)
• Logical data modeling: Entity-relationship diagrams, normalization (1NF through Boyce-Codd/BCNF), denormalization trade-offs; identify functional dependencies and anomalies.
• Physical data modeling: Table design, partitioning strategies, indexes; SCD types; dimensional vs. transactional schemas; storage patterns for OLTP vs. analytics.
• Normalization & design: Normalize to 3NF/BCNF for transactional systems; understand when to denormalize for queries; trade-offs between 3NF, Data Vault, and star schemas.
• Domain-Driven Design: Bounded contexts and subdomains; aggregates and aggregate roots; entities vs. value objects; repository patterns; ubiquitous language.
• Event-driven architecture: Domain events and contracts; CDC as event streams; idempotency and replay patterns; mapping Db2 transactions to event-driven architectures; saga orchestration.
• CQRS patterns: Command/query separation; event sourcing and state reconstruction; eventual consistency; when CQRS is justified for mainframe migration vs. overkill.
• Database internals: Index structures (B-tree, bitmap, etc.), query planning, partitioning strategies; how Db2 vs. PostgreSQL differ in storage and execution.
• Data quality & validation: Designing test suites for schema conformance; referential integrity checks; sampling and reconciliation strategies.
